Data 607 Final Project
Wilson Chau
2022-12-01
Introduction:
I am interested in dealing with data that is relevant to my life and how I am feeling. Lately I noticed that a lot of my colleagues are more concern about their own happiness and well being. This lead me to want to focus on contribution to happiness. I found a dataset on kaggle about countries and their happiness being measured.
Coming from a covid lockdown from 2020-2021. I noticed the stress level fo my friends are at an all time high. I understand that happiness does change over time, but with a pandemic level threat like Covid. Was happiness greatly affected? Did government controls or aid provided any relief in contributing happiness to the world from 2020-2021? #Goal and Motivation My motivation for working on this proposal is to determine if happiness level increased from 2020 to 2021 with the ease of the lockdown, or did happiness decrease even further due to the amount of stress developed after the lockdown and covid. I understand government played a role in how our society was able to move. I will explore how the Happiness score varies across countries in different parts of the world. I will also identify the groups of countries(regions) of high happiness across the globe, and the country with highest happiness score in each region. These regions will be highlighted on a world map indicating the happiest and saddest parts of the globe. I want to see I am determined to learn more about happiness, and how to bring it to my own mental health. My personal goal is to showcase my R Programming. APPROACH
In answering the question above, the following approach was followed:
Acquire tech stock data. Filter for highest value (growth or market cap) companies. Verify corresponding company review on Glassdoor (if < 3.5, drop). For each company, scrape the “Pros” section of the top 10 reviews. Tidy and transform our collection of reviews. Visualize most frequent, pertinent verbage via table, barplot, and wordcloud. Analyze and conclude.
Where is the data sources:
I received the dataset for 1) 2020 Happiness level:https://www.kaggle.com/datasets/londeen/world-happiness-report-2020 2) 2021 Happiness level:https://www.kaggle.com/datasets/ajaypalsinghlo/world-happiness-report-2021 Work Citation: 3) https://scrumbook.org/retrospective-pattern-language/happiness-metric.html Note: 1) Dataset is uplaoded into github where I extract the raw dataset from here: https://github.com/Wilchau/Data_607_Final_Project
Setting up the data and library
happy_20 <- read.csv("https://raw.githubusercontent.com/Wilchau/Data_607_Final_Project/main/world_happiness_report_2020.csv", header=TRUE)
happy_21 <- read.csv("https://raw.githubusercontent.com/Wilchau/Data_607_Final_Project/main/world_happiness_report_2021.csv", header=TRUE)
library(readr)
library(readxl)
library(dplyr)##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
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## filter, lag## The following objects are masked from 'package:base':
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## intersect, setdiff, setequal, unionlibrary(ggplot2)
library(tidyr)
library(stringr)
library(e1071)
library(tidyverse)## ── Attaching packages
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## tidyverse 1.3.2 ──## ✔ tibble 3.1.8 ✔ forcats 0.5.2
## ✔ purrr 0.3.4
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()library(corrplot)## corrplot 0.92 loadedlibrary(RColorBrewer)
library(Hmisc)## Loading required package: lattice
## Loading required package: survival
## Loading required package: Formula
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## Attaching package: 'Hmisc'
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## The following object is masked from 'package:e1071':
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## impute
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## The following objects are masked from 'package:dplyr':
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## src, summarize
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## The following objects are masked from 'package:base':
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## format.pval, unitslibrary(ggpubr)head(happy_20)## Country.name Regional.indicator Ladder.score Standard.error.of.ladder.score
## 1 Finland Western Europe 7.8087 0.03115630
## 2 Denmark Western Europe 7.6456 0.03349229
## 3 Switzerland Western Europe 7.5599 0.03501417
## 4 Iceland Western Europe 7.5045 0.05961586
## 5 Norway Western Europe 7.4880 0.03483738
## 6 Netherlands Western Europe 7.4489 0.02779175
## upperwhisker lowerwhisker Logged.GDP.per.capita Social.support
## 1 7.869766 7.747634 10.63927 0.9543297
## 2 7.711245 7.579955 10.77400 0.9559908
## 3 7.628528 7.491272 10.97993 0.9428466
## 4 7.621347 7.387653 10.77256 0.9746696
## 5 7.556281 7.419719 11.08780 0.9524866
## 6 7.503372 7.394428 10.81271 0.9391388
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 71.90083 0.9491722 -0.05948202
## 2 72.40250 0.9514443 0.06620178
## 3 74.10245 0.9213367 0.10591104
## 4 73.00000 0.9488919 0.24694422
## 5 73.20078 0.9557503 0.13453263
## 6 72.30092 0.9085478 0.20761244
## Perceptions.of.corruption Ladder.score.in.Dystopia
## 1 0.1954446 1.972317
## 2 0.1684895 1.972317
## 3 0.3037284 1.972317
## 4 0.7117097 1.972317
## 5 0.2632182 1.972317
## 6 0.3647171 1.972317
## Explained.by..Log.GDP.per.capita Explained.by..Social.support
## 1 1.285190 1.499526
## 2 1.326949 1.503449
## 3 1.390774 1.472403
## 4 1.326502 1.547567
## 5 1.424207 1.495173
## 6 1.338946 1.463646
## Explained.by..Healthy.life.expectancy
## 1 0.9612714
## 2 0.9793326
## 3 1.0405332
## 4 1.0008434
## 5 1.0080719
## 6 0.9756753
## Explained.by..Freedom.to.make.life.choices Explained.by..Generosity
## 1 0.6623167 0.1596704
## 2 0.6650399 0.2427934
## 3 0.6289545 0.2690558
## 4 0.6619807 0.3623302
## 5 0.6702009 0.2879851
## 6 0.6136265 0.3363176
## Explained.by..Perceptions.of.corruption Dystopia...residual
## 1 0.4778573 2.762835
## 2 0.4952603 2.432741
## 3 0.4079459 2.350267
## 4 0.1445408 2.460688
## 5 0.4341006 2.168266
## 6 0.3685698 2.352117head(happy_21)## Country.name Regional.indicator Ladder.score Standard.error.of.ladder.score
## 1 Finland Western Europe 7.842 0.032
## 2 Denmark Western Europe 7.620 0.035
## 3 Switzerland Western Europe 7.571 0.036
## 4 Iceland Western Europe 7.554 0.059
## 5 Netherlands Western Europe 7.464 0.027
## 6 Norway Western Europe 7.392 0.035
## upperwhisker lowerwhisker Logged.GDP.per.capita Social.support
## 1 7.904 7.780 10.775 0.954
## 2 7.687 7.552 10.933 0.954
## 3 7.643 7.500 11.117 0.942
## 4 7.670 7.438 10.878 0.983
## 5 7.518 7.410 10.932 0.942
## 6 7.462 7.323 11.053 0.954
## Healthy.life.expectancy Freedom.to.make.life.choices Generosity
## 1 72.0 0.949 -0.098
## 2 72.7 0.946 0.030
## 3 74.4 0.919 0.025
## 4 73.0 0.955 0.160
## 5 72.4 0.913 0.175
## 6 73.3 0.960 0.093
## Perceptions.of.corruption Ladder.score.in.Dystopia
## 1 0.186 2.43
## 2 0.179 2.43
## 3 0.292 2.43
## 4 0.673 2.43
## 5 0.338 2.43
## 6 0.270 2.43
## Explained.by..Log.GDP.per.capita Explained.by..Social.support
## 1 1.446 1.106
## 2 1.502 1.108
## 3 1.566 1.079
## 4 1.482 1.172
## 5 1.501 1.079
## 6 1.543 1.108
## Explained.by..Healthy.life.expectancy
## 1 0.741
## 2 0.763
## 3 0.816
## 4 0.772
## 5 0.753
## 6 0.782
## Explained.by..Freedom.to.make.life.choices Explained.by..Generosity
## 1 0.691 0.124
## 2 0.686 0.208
## 3 0.653 0.204
## 4 0.698 0.293
## 5 0.647 0.302
## 6 0.703 0.249
## Explained.by..Perceptions.of.corruption Dystopia...residual
## 1 0.481 3.253
## 2 0.485 2.868
## 3 0.413 2.839
## 4 0.170 2.967
## 5 0.384 2.798
## 6 0.427 2.580Data Cleaning for 2020 and 2021 In order to focus more on the variables
that contribute to happiness. I will take the Country.name, Regional.indicator., Ladder.score Logged.GDP.per.capita, Social.support, Freedom.to.make.life.choices, Generosity, Perceptions.of.corruption, Dystopia…residual Column:(1,2,7,8,9,10,11,12,20)
New_20 <- happy_20 %>% select(1, 2, 3, 7,8, 9, 10, 11, 12, 20)
New_21 <- happy_21 %>% select(1, 2, 3, 7,8, 9, 10, 11, 12, 20)I will check to see if there is any missing values in the data set, which has no missing values. Once there aren’t any missing values, I check the new database New_20 and New_21 where I wanted to
sum(is.na(New_20))## [1] 0sum(is.na(New_21))## [1] 0Statistical analysis
summary(New_20)## Country.name Regional.indicator Ladder.score Logged.GDP.per.capita
## Length:153 Length:153 Min. :2.567 Min. : 6.493
## Class :character Class :character 1st Qu.:4.724 1st Qu.: 8.351
## Mode :character Mode :character Median :5.515 Median : 9.456
## Mean :5.473 Mean : 9.296
## 3rd Qu.:6.229 3rd Qu.:10.265
## Max. :7.809 Max. :11.451
## Social.support Healthy.life.expectancy Freedom.to.make.life.choices
## Min. :0.3195 Min. :45.20 Min. :0.3966
## 1st Qu.:0.7372 1st Qu.:58.96 1st Qu.:0.7148
## Median :0.8292 Median :66.31 Median :0.7998
## Mean :0.8087 Mean :64.45 Mean :0.7834
## 3rd Qu.:0.9067 3rd Qu.:69.29 3rd Qu.:0.8777
## Max. :0.9747 Max. :76.80 Max. :0.9750
## Generosity Perceptions.of.corruption Dystopia...residual
## Min. :-0.30091 Min. :0.1098 Min. :0.2572
## 1st Qu.:-0.12701 1st Qu.:0.6830 1st Qu.:1.6299
## Median :-0.03366 Median :0.7831 Median :2.0463
## Mean :-0.01457 Mean :0.7331 Mean :1.9723
## 3rd Qu.: 0.08543 3rd Qu.:0.8492 3rd Qu.:2.3503
## Max. : 0.56066 Max. :0.9356 Max. :3.4408summary(New_21)## Country.name Regional.indicator Ladder.score Logged.GDP.per.capita
## Length:149 Length:149 Min. :2.523 Min. : 6.635
## Class :character Class :character 1st Qu.:4.852 1st Qu.: 8.541
## Mode :character Mode :character Median :5.534 Median : 9.569
## Mean :5.533 Mean : 9.432
## 3rd Qu.:6.255 3rd Qu.:10.421
## Max. :7.842 Max. :11.647
## Social.support Healthy.life.expectancy Freedom.to.make.life.choices
## Min. :0.4630 Min. :48.48 Min. :0.3820
## 1st Qu.:0.7500 1st Qu.:59.80 1st Qu.:0.7180
## Median :0.8320 Median :66.60 Median :0.8040
## Mean :0.8147 Mean :64.99 Mean :0.7916
## 3rd Qu.:0.9050 3rd Qu.:69.60 3rd Qu.:0.8770
## Max. :0.9830 Max. :76.95 Max. :0.9700
## Generosity Perceptions.of.corruption Dystopia...residual
## Min. :-0.28800 Min. :0.0820 Min. :0.648
## 1st Qu.:-0.12600 1st Qu.:0.6670 1st Qu.:2.138
## Median :-0.03600 Median :0.7810 Median :2.509
## Mean :-0.01513 Mean :0.7274 Mean :2.430
## 3rd Qu.: 0.07900 3rd Qu.:0.8450 3rd Qu.:2.794
## Max. : 0.54200 Max. :0.9390 Max. :3.482Data Summary Originally the data set has: happy_20 -> New_20 Number of
variables: 10 Number of countries: 153 happy_21 -> New_21 Number of variables: 10 Number of Countries: 149
The Ladder score is the main indicator of “Happiness” Level. Based on the summary function, we can see that in 2020 the max ladder score is 7.809 while in 2021 it is 7.842. We need in 2021 the high increase on Social support, GDP. Capital, Health Expectancy gives a better picture of mental health for everyone…. The one stress that I see still are an increase of Perceptions of corruption, and Dystopia Residual from 2020 -> 2021.Decrease in Freedom to make a choice and Generosity from 2020 -> 2021 can reveal many types of unhappiness in our life.
Different type of presentation
ggplot(happy_21, aes(x = Healthy.life.expectancy, y = Freedom.to.make.life.choices)) +
geom_point() +
stat_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(happy_21, aes(x = Healthy.life.expectancy, y = Freedom.to.make.life.choices)) +
geom_point() +
stat_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(happy_20, aes(x = Healthy.life.expectancy, y = Social.support)) +
geom_point() +
stat_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
ggplot(happy_21, aes(x = Healthy.life.expectancy, y = Social.support)) +
geom_point() +
stat_smooth()## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
corrplot(cor(happy_20 %>%
select(Ladder.score,Social.support, Freedom.to.make.life.choices,Logged.GDP.per.capita, Generosity)),
method="color",
sig.level = 0.01, insig = "blank",
addCoef.col = "black",
tl.srt=45,
type="upper")
corrplot(cor(happy_21 %>%
select(Ladder.score,Social.support, Freedom.to.make.life.choices,Logged.GDP.per.capita, Generosity)),
method="color",
sig.level = 0.01, insig = "blank",
addCoef.col = "black",
tl.srt=45,
type="upper")
``` ##Observation
We can see from results 2020 and 2021 that Freedom to make life choices, and social support is viewed as something that is greatly expressive in making sure it can contribute to our happiness. On the correlation map, you can see Social support, Freedom to make life choices, GDP per capita and genersity comes to play as well.
#Conclusion Happiness is defined by a many variables, but through professional studies we can see that when we look through happiness, social support, freedom to make life choices, capita, and generosity plays a huge role in making sure this can help us gain the happiness we can contribute as a society.
#work citation